Quantum-Machine-Assisted Drug Discovery: Survey and Perspective
Abstract
Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integration of quantum computing into drug discovery and development, focusing on how quantum technologies might accelerate and enhance various stages of the drug development cycle. Specifically, we explore the application of quantum computing in addressing challenges related to drug discovery, such as molecular simulation and the prediction of drug-target interactions, as well as the optimization of clinical trial outcomes. By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market, ultimately benefiting public health.
Authors 8
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Rensselaer Polytechnic Institute
Affiliation as printed
Rensselaer Polytechnic Institute, Troy, NY, USA
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Affiliation as printed
Novo Nordisk A/S, Måløv, Denmark
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Affiliation as printed
Instituut-Lorentz, Universiteit Leiden, The Netherlands
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Zhiding Liang corresponding
Rensselaer Polytechnic Institute
Affiliation as printed
Rensselaer Polytechnic Institute, Troy, NY, USA
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Affiliation as printed
Harvard University, Cambridge, MA, USA
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Affiliation as printed
University of Chicago, Chicago, IL, USA
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Junyu Liu corresponding
Affiliation as printed
University of Pittsburgh, Pittsburgh, PA, USA
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Tianfan Fu corresponding
Rensselaer Polytechnic Institute
Affiliation as printed
Rensselaer Polytechnic Institute, Troy, NY, USA
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